
Unlike OFA [1]}, DONNA enables hardware-aware search in diverse search spaces; differentiable and RL-/evolutionary-based methods can do this too, but using much more memory or training time, respectively.

DONNA scales to multiple accuracy/latency targets, requiring only marginal cost for every new target. This is in contrast with differentiable or RL-/evolutionary-based methods, where the search has to be repeated for every new target.

DONNA uses a novel accuracy predictor which correlates better with training-from-scratch accuracy than prior work like DNA [2]} (See Figure REF ).

Furthermore, the DONNA accuracy predictor generalizes to unseen search spaces due to its reliance on block quality metrics, not on the network configuration (See Figure REF ).

DONNA relies on a fast finetuning method that achieves the same accuracy as training-from-scratch while being \(9\times \)  faster, reducing the training time for found architectures compared to DNA [2]}.

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